基于MAD约束模式树的离群序列周期性检测
Periodicity Detection of Outlier Sequences Using Constraint Based Pattern Tree with MAD
摘要：
在数据中很少出现或不常见的模式可以被定义为离群模式。检测离群模式的基本思想是将它们的相对频率与频繁模式进行比较。它们出现的频率较低，因此在数据中的支持度较低。异常模式检测是一项重要的数据采掘任务，它将揭示一些有趣的事实。对模式周期性的搜索给出了这些模式在时间上的行为，即它们何时可能重复。这反过来又有助于预测事件。这些模式被发现在时间序列数据，社交网络等。在本文中，提出了一种算法的周期性离群模式检测使用基于约束的FP（频繁模式）树作为底层数据结构的时间序列数据。树的增长是有限的，使用水平和单调约束。收集名为大肠杆菌的细菌的蛋白质序列，并识别序列中的周期性离群模式。此外，通过在定义候选离群点模式时找到中值绝对偏差（MAD）来增强结果。最后给出了STNR-out（SuffixTreeNoiseResilientforOutlierDetection）算法与本文算法的比较结果。结果表明了该算法的有效性和适用性。
英文摘要：
Patterns that appear rarely or unusually in the data can be defined as outlier patterns. The basic idea behind detecting outlier patterns is comparison of their relative frequencies with frequent patterns. Their frequencies of appearance are less and thus have lesser support in the data. Detecting outlier patterns is an important data mining task which will reveal some interesting facts. The search for periodicity of patterns gives the behavior of these patterns across time as to when they repeat likely. This in turn helps in prediction of events. These patterns are found in Time series-data, social networks etc. In this paper, an algorithm for periodic outlier pattern detection is proposed with the usage of a Constraint Based FP (Frequent Pattern)-tree as the underlying data structure for time series data. The growth of the tree is limited by using level and monotonic constraints. The protein sequence of bacteria named E.Coli is collected and periodic outlier patterns in the sequence are identified. Further the enhancement of results is obtained by finding the Median Absolute Deviation (MAD) in defining candidate outlier patterns. The comparative results between STNR-out (Suffix Tree Noise Resilient for Outlier Detection) and proposed algorithm are illustrated. The results show the effectiveness and applicability of the proposed algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
